Optimization of time series forecasting by combination of models with evolutionary heuristic and error-correlation parameters

Two algorithms for the optimization of time series forecasting by combination of models are proposed and evaluated. The first named GABoost, exploits the heuristic of genetic algorithm in order to search the optimal weights for the mixing of forecasting models. The second named CombFEC, extracts information provided by the forecast errors (RMSE, BE and MAE) of each model to be combined, and the correlation between each model and the forecasted time series, in order to build an error-correlation function (FEC) used to calculate the weights with a SOFTMAX function. The results show that both algorithms are able to improve the forecasting of different time series, reducing the forecast error (RMSE) and increasing the modeling capability expressed by a reduction of the bias error (BE).